耦合振荡器协同性在时间生物学中的控制机制。

IF 9 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Cell Systems Pub Date : 2023-05-17 DOI:10.1016/j.cels.2023.04.001
Mathias S Heltberg, Yuanxu Jiang, Yingying Fan, Zhibo Zhang, Malthe S Nordentoft, Wei Lin, Long Qian, Qi Ouyang, Mogens H Jensen, Ping Wei
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引用次数: 1

摘要

动态过程的控制对于维持正确的细胞调节和细胞命运决定至关重要。许多调节网络表现出振荡行为;然而,当一个振荡器受到两个或多个外部振荡信号的刺激时,我们的知识仍然缺失。我们在酵母中构建了一个合成振荡系统,并用两个外部振荡信号刺激它来探索这个问题。让模型验证和预测与实验观察紧密相互作用,我们发现两个外部信号的刺激扩大了夹带的平台并减少了振荡的波动。此外,通过调节外部信号的相位差,可以控制振荡的幅度,这可以通过无扰动振荡网络的信号延迟来理解。由此,我们揭示了下游基因转录的直接振幅依赖性。综上所述,这些结果为利用耦合振荡器的协同性控制振荡系统提供了一条新的途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Coupled oscillator cooperativity as a control mechanism in chronobiology.

Control of dynamical processes is vital for maintaining correct cell regulation and cell-fate decisions. Numerous regulatory networks show oscillatory behavior; however, our knowledge of how one oscillator behaves when stimulated by two or more external oscillatory signals is still missing. We explore this problem by constructing a synthetic oscillatory system in yeast and stimulate it with two external oscillatory signals. Letting model verification and prediction operate in a tight interplay with experimental observations, we find that stimulation with two external signals expands the plateau of entrainment and reduces the fluctuations of oscillations. Furthermore, by adjusting the phase differences of external signals, one can control the amplitude of oscillations, which is understood through the signal delay of the unperturbed oscillatory network. With this we reveal a direct amplitude dependency of downstream gene transcription. Taken together, these results suggest a new path to control oscillatory systems by coupled oscillator cooperativity.

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来源期刊
Cell Systems
Cell Systems Medicine-Pathology and Forensic Medicine
CiteScore
16.50
自引率
1.10%
发文量
84
审稿时长
42 days
期刊介绍: In 2015, Cell Systems was founded as a platform within Cell Press to showcase innovative research in systems biology. Our primary goal is to investigate complex biological phenomena that cannot be simply explained by basic mathematical principles. While the physical sciences have long successfully tackled such challenges, we have discovered that our most impactful publications often employ quantitative, inference-based methodologies borrowed from the fields of physics, engineering, mathematics, and computer science. We are committed to providing a home for elegant research that addresses fundamental questions in systems biology.
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